{
 "metadata": {
  "name": "",
  "signature": "sha256:de0dfc17222e6c7474084ddf58b5cd5fd1a801a67da87429332188e2a1fdf8a0"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline \n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "import pylab as pl\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "companyinfo = pd.read_csv(\"companysentimentszscore.csv\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#breakdown of sentiments extracted"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure();\n",
      "companyinfo.sentiment.plot(kind='bar'); plt.axhline(0, color='k')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "<matplotlib.lines.Line2D at 0x7f125bb3e9e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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G/r2qHp89Xe+H5vNp+rRdq/fn+aBte9tXdrTtO07T9oyq/+DkKlX/WcpPao/8\npl5Sg151JHQAmIg98ZMZAPYCEjoATAQJHQAmgoQOABNBQgeAifgH3MjLIlP1Vd4AAAAASUVORK5C\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f125bc078d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "altmanres.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "count    3313.000000\n",
        "mean        4.875892\n",
        "std        16.528006\n",
        "min      -157.210000\n",
        "25%         1.372000\n",
        "50%         3.083000\n",
        "75%         5.466000\n",
        "max       454.079000\n",
        "Name: altman, dtype: float64"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "companyinfo.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "id                5787\n",
        "adrTso            5787\n",
        "date_extracted    5787\n",
        "exchange          5787\n",
        "industry          5787\n",
        "ipoYear           5787\n",
        "lastSale          5787\n",
        "marketCap         5787\n",
        "name              5787\n",
        "sector            5787\n",
        "summaryQuote      5787\n",
        "symbol            5787\n",
        "sentiment         3985\n",
        "altman            3313\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#exclude companies in finance\n",
      "#find finance companies\n",
      "financecompanies = companyinfo.sector.isin(['Finance'])\n",
      "companies_excfinance = companyinfo[~financecompanies]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "companies_excfinance.count()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "id                4948\n",
        "adrTso            4948\n",
        "date_extracted    4948\n",
        "exchange          4948\n",
        "industry          4948\n",
        "ipoYear           4948\n",
        "lastSale          4948\n",
        "marketCap         4948\n",
        "name              4948\n",
        "sector            4948\n",
        "summaryQuote      4948\n",
        "symbol            4948\n",
        "sentiment         3496\n",
        "altman            3268\n",
        "dtype: int64"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sentiments = companies_excfinance['sentiment']\n",
      "zscore = companies_excfinance['altman']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nullsentiments = sentiments.isnull()\n",
      "nullzscore = zscore.isnull()\n",
      "clnsentiments = sentiments.copy()\n",
      "clnsentiments[nullsentiments]= 2\n",
      "clnzscore = zscore.copy()\n",
      "clnzscore[nullzscore] = 2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from scipy.stats.stats import pearsonr "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampledcompanies = companies_excfinance[~(companies_excfinance.sentiment.isnull()) & ~(companies_excfinance.altman.isnull())]\n",
      "print (pearsonr(sampledcompanies.altman, sampledcompanies.sentiment))\n",
      "print(sampledcompanies.count())\n",
      "df = pd.DataFrame(sampledcompanies)\n",
      "pl.scatter(sampledcompanies.altman, sampledcompanies.sentiment)\n",
      "plot(x='altman', y='sentiment',kind='scatter')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(-0.015231856040409667, 0.43596077120838006)\n",
        "id                2618\n",
        "adrTso            2618\n",
        "date_extracted    2618\n",
        "exchange          2618\n",
        "industry          2618\n",
        "ipoYear           2618\n",
        "lastSale          2618\n",
        "marketCap         2618\n",
        "name              2618\n",
        "sector            2618\n",
        "summaryQuote      2618\n",
        "symbol            2618\n",
        "sentiment         2618\n",
        "altman            2618\n",
        "dtype: int64\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "[]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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h2AEgMAQ7AASGYAeAwBDsABAYgh0AAkOwA0BgCHYACAzBDgCBIdgBIDCpg93MDpnZ02b2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\ngMAQ7AAQGIIdAAJDsANAYAh2AAjM/wOlgapFp31ZnwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fab43fcc1d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "notneutral=sampledcompanies[~(sampledcompanies.sentiment==2) & ~(sampledcompanies.altman.between(1.8, 3.0))]\n",
      "print (pearsonr(notneutral.altman, notneutral.sentiment))\n",
      "plot(notneutral.altman, notneutral.sentiment,type=\"scattered\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(-0.01912168648383112, 0.69118814198285561)\n"
       ]
      },
      {
       "ename": "TypeError",
       "evalue": "There is no line property \"type\"",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-104-33062c98b854>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mnotneutral\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msampledcompanies\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m~\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msampledcompanies\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msentiment\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m~\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msampledcompanies\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maltman\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbetween\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1.8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3.0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mpearsonr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnotneutral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maltman\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnotneutral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msentiment\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnotneutral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maltman\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnotneutral\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msentiment\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"scattered\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m   3097\u001b[0m         \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3098\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3099\u001b[1;33m         \u001b[0mret\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3100\u001b[0m         \u001b[0mdraw_if_interactive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3101\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1372\u001b[0m         \u001b[0mlines\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1373\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1374\u001b[1;33m         \u001b[1;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1375\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1376\u001b[0m             \u001b[0mlines\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    301\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    302\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m                 \u001b[1;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mremaining\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    304\u001b[0m                     \u001b[1;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    305\u001b[0m                 \u001b[1;32mreturn\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[1;34m(self, tup, kwargs)\u001b[0m\n\u001b[0;32m    289\u001b[0m         \u001b[0mncx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncy\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    290\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mj\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mxrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mncx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 291\u001b[1;33m             \u001b[0mseg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncx\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mj\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mncy\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    292\u001b[0m             \u001b[0mret\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    293\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mret\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_makeline\u001b[1;34m(self, x, y, kw, kwargs)\u001b[0m\n\u001b[0;32m    241\u001b[0m                             \u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    242\u001b[0m                             )\n\u001b[1;32m--> 243\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_lineprops\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mseg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    244\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mseg\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    245\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/lib/python3.4/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36mset_lineprops\u001b[1;34m(self, line, **kwargs)\u001b[0m\n\u001b[0;32m    182\u001b[0m             \u001b[0mfuncName\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"set_%s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    183\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfuncName\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 184\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'There is no line property \"%s\"'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    185\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfuncName\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    186\u001b[0m             \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mval\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mTypeError\u001b[0m: There is no line property \"type\""
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
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       "text": [
        "<matplotlib.figure.Figure at 0x7f156c87a940>"
       ]
      }
     ],
     "prompt_number": 104
    }
   ],
   "metadata": {}
  }
 ]
}